Outlier detection method for geotechnical engineering based on MetaOD model selection

Abstract

The geotechnical engineering field and indoor parameter test data are the foundation of engineering construction, design and evaluation. The existence of abnormal data often misleads the determination of parameters such as construction and design. Data anomaly detection is the most basic but extremely important task to ensure the safety and reliability of a project. Aiming at the blindness of detection due to the lack of model selection in traditional anomaly detection algorithms, this paper proposes an anomaly detection model system based on a combination of meta-learning outlier detection (MetaOD) and data mining algorithms. The system first selects the initial model class and its parameters suitable for different data types according to the characteristics of the data, averages the selected parameters of the same type of algorithm, and then uses the selected algorithm to diagnose data anomalies, thereby improving the anomaly accuracy of detection. To evaluate the effectiveness of the model, the machine learning test dataset (glass dataset) proposed by the University of California Irvine, is used for test analysis. The results show that the accuracy rate of anomaly detection using this model system reaches 96.41%, which is much higher than that of other detection algorithms. Finally, the model system is applied to the uniaxial compressive strength dataset of the Macau granite and the groundwater monitoring data of the Junchang Tunnel to carry out anomaly detection and analysis and to identify 9 and 10 abnormal points, respectively.

Keywords

• geotechnical engineering anomaly detection
• metaod algorithm
• model selection
• data mining